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:: Volume 22, Issue 2 (4-2024) ::
Int J Radiat Res 2024, 22(2): 347-353 Back to browse issues page
Spectral CT based radiomics for predicting brain metastases in patients with lung cancer
S. Cao , Z. Shu
Department of Radiology, Shanghai Traditional Chinese Medicine-Integrated Hospital, Shanghai 200082, China , cao906436987@163.com
Abstract:   (456 Views)
Background: The goal of this study was to create a prediction model for brain metastasis (BrMs) in patients with lung cancer using unenhanced spectral computed tomography (CT) and radiomics. Materials and Methods: This study comprised 162 patients with lung cancer who underwent spectral CT from 2019–2021. Patients were split into training and test sets and into BrMs and BrMs-free groups. Spectral and radiomics parameters were obtained from the spectral CT images before pathological confirmation. Prediction models in the training and test sets were created using logistic regression. The receiver operating characteristic curve was used to evaluate each quantitative parameter for predicting BrMs. The diagnostic effectiveness of several parameters was analyzed and compared using the area under the curve (AUC) calculation. The final model was obtained using the Delong test. Results: There were statistically significant differences in the iodine concentrations and the slope of the energy spectrum attenuation curve of the two groups <(p0.05). The AUC of the combined radiomics model was greater than that of the 70 keV and 120 keV sequence models. The joint parameters of radiomics and spectral CT constructed an integrated model. In the training set, test set, and overall set, the AUCs of the integrated model were 0.875, 0.879, and 0.724, respectively. In the training and overall sets, the prediction performance of the integrated model outperformed the spectral and radiomics models (p<0.05). Conclusions: This integrated model may predict the BrMs in lung cancer patients.
Keywords: Prediction model, spectral CT, radiomics, lung cancer, brain metastasis.
Full-Text [PDF 1586 kb]   (146 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. 1. McGuire S (2015) World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press. Adv Nutr, 7: 418-9. [DOI:10.3945/an.116.012211]
2. Sung H, Ferlay J, Siegel RL, et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, 71:209-249. [DOI:10.3322/caac.21660]
3. Page S, Milner-Watts C, Perna M, et al. (2020) Systemic treatment of brain metastases in non-small cell lung cancer. Eur J Cancer, 132:187-198. [DOI:10.1016/j.ejca.2020.03.006]
4. Sperduto PW, Kased N, Roberge D, et al. (2012) Summary report on the graded prognostic assessment: an accurate and facile diagnosis-specific tool to estimate survival for patients with brain metastases. J Clin Oncol, 30:419e25. [DOI:10.1200/JCO.2011.38.0527]
5. Sacks P and Rahman M (2020) Epidemiology of brain metastases. Neurosurg Clin N A, 31:481-488. [DOI:10.1016/j.nec.2020.06.001]
6. Wen Q, Yue Y, Shang J, et al. (2021) The application of dual-layer spectral detector computed tomography in solitary pulmonary nodule identification. Quant Imaging Med Surg, 11:521-532. [DOI:10.21037/qims-20-2]
7. Fehrenbach U, Kahn J, Böning G, et al. (2019) Spectral CT and its specific values in the staging of patients with non-small cell lung cancer: technical possibilities and clinical impact. Clin Radiol, 74456-466. [DOI:10.1016/j.crad.2019.02.010]
8. Zhu Q, Ren C, Zhang Y, et al. (2020) Comparative imaging study of mediastinal lymph node from pre-surgery dual energy CT versus post-surgeron verifications in non-small cell lung cancer patients. J Peking Univ Health Sci, 52: 730-737.
9. Zhang G, Cao Y, Zhang J, et al. (2021) Epidermal growth factor receptor mutations in lung adenocarcinoma: associations between dual-energy spectral CT measurements and histologic results. J Cancer Res Clin Oncol, 147:1169-1178. [DOI:10.1007/s00432-020-03402-8]
10. Li Q, Li X, Li XY, et al. (2020) Spectral CT in Lung Cancer: Usefulness of Iodine Concentration for Evaluation of Tumor Angiogenesis and Prognosis. AJR Am J Roentgenol, 215:595-602. [DOI:10.2214/AJR.19.22688]
11. Yu Y, Cheng JJ, Li JY, et al. (2020) Determining the invasiveness of pure ground-glass nodules using dual-energy spectral computed tomography. Transl Lung Cancer Res, 9: 484-495. [DOI:10.21037/tlcr.2020.03.33]
12. Lambin P, Rios-Velazquez E, Leijenaar R, et al. (2012) Radiomics: extracting more information from medical images using advanced feature analysis [J]. Eur J Cancer, 48:441-446. [DOI:10.1016/j.ejca.2011.11.036]
13. Song J, Yin Y, Wang H, et al. (2020) A review of original articles published in the emerging field of radiomics[J]. Eur J Radiol, 127: 108991. [DOI:10.1016/j.ejrad.2020.108991]
14. Sun F, Chen Y, Chen X, et al. (2021) CT-based radiomics for predicting brain metastases as the first failure in patients with curatively resected locally advanced non-small cell lung cancer. Eur J Radiol, 134:109411. [DOI:10.1016/j.ejrad.2020.109411]
15. Lennartz S, Mager A, Große Hokamp N, et al. (2021) Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules. Cancer Imaging, 21:17. [DOI:10.1186/s40644-020-00374-3]
16. Yang F, Dong J, Wang X, et al. (2017) Non-small cell lung cancer: Spectral computed tomography quantitative parameters for preoperative diagnosis of metastatic lymph nodes. Eur J Radiol, 89:129-135. [DOI:10.1016/j.ejrad.2017.01.026]
17. Yue D, Ru Xin W, Jing C, et al. (2017) Virtual monochromatic spectral imaging for the evaluation of vertebral inconspicuous osteoblastic metastases from lung. Acta Radiol, 58:1485-1492. [DOI:10.1177/0284185117694511]
18. Won YW, Joo J, Yun T, et al. (2015) A nomogram to predict brain metastasis as the first relapse in curatively resected non-small cell lung cancer patients. Lung Cancer, 88: 201-7. [DOI:10.1016/j.lungcan.2015.02.006]
19. Wang H, Wang Z, Zhang G, et al. (2020) Driver genes as predictive indicators of brain metastasis in patients with advanced NSCLC: EGFR, ALK, and RET gene mutations. Cancer Med, 9:487-495. [DOI:10.1002/cam4.2706]
20. Sung P, Yoon SH, Kim J, et al. (2021) Bronchovascular bundle thickening on CT as a predictor of survival and brain metastasis in patients with stage IA peripheral small cell lung cancer. Clin Radiol, 76:76.e37-76.e46. [DOI:10.1016/j.crad.2020.08.018]
21. Hwang KE, Oh SJ, Park C, et al. (2018) Computed tomography morphologic features of pulmonary adenocarcinoma with brain/bone metastasis. Korean J Intern Med, 33:340-346. [DOI:10.3904/kjim.2016.134]
22. Wei XG, Bi KW, Li B (2021) Phenotypic Plasticity Conferred by the Metastatic Microenvironment of the Brain Strengthens the Intracranial Tumorigenicity of Lung Tumor Cells. Front Oncol, 11: 637911. [DOI:10.3389/fonc.2021.637911]
23. Chen J, Liu A, Lin Z, et al. (2020) Downregulation of the circadian rhythm regulator HLF promotes multiple-organ distant metastases in non-small cell lung cancer through PPAR/NF-κb signaling. Cancer Lett, 482: 56-71. [DOI:10.1016/j.canlet.2020.04.007]
24. Zhang J, Jin J, Ai Y, Zhu K, Xiao C, Xie C, Jin X. (2021) Computer Tomography Radiomics-Based Nomogram in the Survival Prediction for Brain Metastases From Non-Small Cell Lung Cancer Underwent Whole Brain Radiotherapy. Front Oncol, 10:610691. [DOI:10.3389/fonc.2020.610691]
25. Zhao S, Hou D, Zheng X, et al. (2021) MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer. Transl Lung Cancer Res, 10: 368-380. [DOI:10.21037/tlcr-20-361]
26. Huang Y, Liu Z, He L, et al. (2016) Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. Radiology, 281:947-957 [DOI:10.1148/radiol.2016152234]
27. Chen A, Lu L, Pu X, et al. (2019) CT-Based Radiomics Model for Predicting Brain Metastasis in Category T1 Lung Adenocarcinoma. AJR Am J Roentgenol, 213:134-139. [DOI:10.2214/AJR.18.20591]
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Cao S, Shu Z. Spectral CT based radiomics for predicting brain metastases in patients with lung cancer. Int J Radiat Res 2024; 22 (2) :347-353
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Volume 22, Issue 2 (4-2024) Back to browse issues page
International Journal of Radiation Research
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